feat: supported batching for Titan text and image embeddings

aidial_adapter_bedrock/dial_api/response.py

@@ -5,6 +5,8 @@
 from aidial_sdk.embeddings import Usage
 from pydantic import BaseModel
 
+from aidial_adapter_bedrock.embedding.encoding import vector_to_base64
+
 
 class ModelObject(BaseModel):
     object: Literal["model"] = "model"
@@ -16,13 +18,31 @@ class ModelsResponse(BaseModel):
     data: List[ModelObject]
 
 
+def _encode_vector(
+    encoding_format: Literal["float", "base64"], vector: List[float]
+) -> List[float] | str:
+    return vector_to_base64(vector) if encoding_format == "base64" else vector
+
+
 def make_embeddings_response(
-    model: str, vectors: List[List[float] | str], usage: Usage
+    model: str,
+    encoding_format: Literal["float", "base64"],
+    vectors: List[List[float]],
+    prompt_tokens: int,
 ) -> EmbeddingsResponse:
 
+    embeddings = [_encode_vector(encoding_format, v) for v in vectors]
+
     data: List[Embedding] = [
         Embedding(index=index, embedding=embedding)
-        for index, embedding in enumerate(vectors)
+        for index, embedding in enumerate(embeddings)
     ]
 
-    return EmbeddingsResponse(model=model, data=data, usage=usage)
+    return EmbeddingsResponse(
+        model=model,
+        data=data,
+        usage=Usage(
+            prompt_tokens=prompt_tokens,
+            total_tokens=prompt_tokens,
+        ),
+    )

aidial_adapter_bedrock/embedding/amazon/titan_image.py

@@ -5,11 +5,11 @@
 https://github.com/aws-samples/amazon-bedrock-samples/blob/5752afb78e7fab49cfd42d38bb09d40756bf0ea0/multimodal/Titan/titan-multimodal-embeddings/rag/1_multimodal_rag.ipynb
 """
 
-from typing import AsyncIterator, List, Self
+import asyncio
+from typing import AsyncIterator, List, Self, Tuple
 
 from aidial_sdk.chat_completion import Attachment
 from aidial_sdk.embeddings import Response as EmbeddingsResponse
-from aidial_sdk.embeddings import Usage
 from aidial_sdk.embeddings.request import EmbeddingsRequest
 from pydantic import BaseModel
 
@@ -30,7 +30,6 @@
 from aidial_adapter_bedrock.embedding.embeddings_adapter import (
     EmbeddingsAdapter,
 )
-from aidial_adapter_bedrock.embedding.encoding import vector_to_base64
 from aidial_adapter_bedrock.embedding.validation import (
     validate_embeddings_request,
 )
@@ -155,31 +154,27 @@ async def embeddings(
             supports_dimensions=True,
         )
 
-        vectors: List[List[float] | str] = []
-        token_count = 0
-
-        # NOTE: Amazon Titan doesn't support batched inputs
-        # TODO: create multiple tasks
-        async for sub_request in get_requests(self.storage, request):
+        async def compute_embeddings(
+            req: AmazonRequest,
+        ) -> Tuple[List[float], int]:
             embedding, text_tokens = await call_embedding_model(
                 self.client,
                 self.model,
-                create_titan_request(sub_request, request.dimensions),
-            )
-
-            image_tokens = sub_request.get_image_tokens()
-
-            vector = (
-                vector_to_base64(embedding)
-                if request.encoding_format == "base64"
-                else embedding
+                create_titan_request(req, request.dimensions),
             )
+            image_tokens = req.get_image_tokens()
+            return embedding, text_tokens + image_tokens
 
-            vectors.append(vector)
-            token_count += text_tokens + image_tokens
+        # NOTE: Amazon Titan doesn't support batched inputs
+        tasks = [
+            asyncio.create_task(compute_embeddings(req))
+            async for req in get_requests(self.storage, request)
+        ]
+        results = await asyncio.gather(*tasks)
 
         return make_embeddings_response(
             model=self.model,
-            vectors=vectors,
-            usage=Usage(prompt_tokens=token_count, total_tokens=token_count),
+            encoding_format=request.encoding_format,
+            vectors=[r[0] for r in results],
+            prompt_tokens=sum(r[1] for r in results),
         )

aidial_adapter_bedrock/embedding/amazon/titan_text.py

@@ -5,10 +5,10 @@
 https://github.com/aws-samples/amazon-bedrock-samples/blob/5752afb78e7fab49cfd42d38bb09d40756bf0ea0/multimodal/Titan/embeddings/v2/Titan-V2-Embeddings.ipynb
 """
 
-from typing import AsyncIterator, List, Self
+import asyncio
+from typing import AsyncIterator, List, Self, Tuple
 
 from aidial_sdk.embeddings import Response as EmbeddingsResponse
-from aidial_sdk.embeddings import Usage
 from aidial_sdk.embeddings.request import EmbeddingsRequest
 
 from aidial_adapter_bedrock.bedrock import Bedrock
@@ -23,7 +23,6 @@
 from aidial_adapter_bedrock.embedding.embeddings_adapter import (
     EmbeddingsAdapter,
 )
-from aidial_adapter_bedrock.embedding.encoding import vector_to_base64
 from aidial_adapter_bedrock.embedding.validation import (
     validate_embeddings_request,
 )
@@ -74,27 +73,23 @@ async def embeddings(
             supports_dimensions=self.supports_dimensions,
         )
 
-        vectors: List[List[float] | str] = []
-        token_count = 0
-
-        # NOTE: Amazon Titan doesn't support batched inputs
-        async for text_input in get_text_inputs(request):
-            sub_request = create_titan_request(text_input, request.dimensions)
-            embedding, tokens = await call_embedding_model(
-                self.client, self.model, sub_request
+        async def compute_embeddings(req: str) -> Tuple[List[float], int]:
+            return await call_embedding_model(
+                self.client,
+                self.model,
+                create_titan_request(req, request.dimensions),
             )
 
-            vector = (
-                vector_to_base64(embedding)
-                if request.encoding_format == "base64"
-                else embedding
-            )
-
-            vectors.append(vector)
-            token_count += tokens
+        # NOTE: Amazon Titan doesn't support batched inputs
+        tasks = [
+            asyncio.create_task(compute_embeddings(req))
+            async for req in get_text_inputs(request)
+        ]
+        results = await asyncio.gather(*tasks)
 
         return make_embeddings_response(
             model=self.model,
-            vectors=vectors,
-            usage=Usage(prompt_tokens=token_count, total_tokens=token_count),
+            encoding_format=request.encoding_format,
+            vectors=[r[0] for r in results],
+            prompt_tokens=sum(r[1] for r in results),
         )

aidial_adapter_bedrock/embedding/cohere/embed_text.py

@@ -9,7 +9,6 @@
 from typing import AsyncIterator, List, Self
 
 from aidial_sdk.embeddings import Response as EmbeddingsResponse
-from aidial_sdk.embeddings import Usage
 from aidial_sdk.embeddings.request import EmbeddingsRequest
 
 from aidial_adapter_bedrock.bedrock import Bedrock
@@ -24,7 +23,6 @@
 from aidial_adapter_bedrock.embedding.embeddings_adapter import (
     EmbeddingsAdapter,
 )
-from aidial_adapter_bedrock.embedding.encoding import vector_to_base64
 from aidial_adapter_bedrock.embedding.validation import (
     validate_embeddings_request,
 )
@@ -92,17 +90,9 @@ async def embeddings(
             self.client, self.model, embedding_request
         )
 
-        vectors: List[List[float] | str] = [
-            (
-                vector_to_base64(embedding)
-                if request.encoding_format == "base64"
-                else embedding
-            )
-            for embedding in embeddings
-        ]
-
         return make_embeddings_response(
             model=self.model,
-            vectors=vectors,
-            usage=Usage(prompt_tokens=tokens, total_tokens=tokens),
+            encoding_format=request.encoding_format,
+            vectors=embeddings,
+            prompt_tokens=tokens,
         )

aidial_adapter_bedrock/llm/model/llama/v3.py

@@ -1,4 +1,5 @@
 import json
+from typing import Awaitable, Callable
 
 from aidial_adapter_bedrock.dial_api.request import ModelParameters
 from aidial_adapter_bedrock.llm.converse.adapter import (
@@ -26,7 +27,7 @@ def is_stream(self, params: ModelParameters) -> bool:
 
 def input_tokenizer_factory(
     deployment: ConverseDeployment, params: ConverseRequestWrapper
-):
+) -> Callable[[ConverseMessages], Awaitable[int]]:
     tool_tokens = default_tokenize_string(json.dumps(params.toolConfig))
     system_tokens = default_tokenize_string(json.dumps(params.system))